Presence Of Noise In Dualistic Sub-Image Histogram Equalization Technique of Image Enhancement

Presence Of Noise In Dualistic Sub-Image Histogram Equalization Technique of Image Enhancement

Sandeep Kumar ,Puneet Verma, Manoj Dahiya,Aa kash Gupta Deptt. Of ECE, Hindu College of Engineering,Sonipat,India Deptt. Of ECE, Hindu College of Engineering,Sonipat,India Deptt. Of ECE, Hindu College of Engineering,Sonipat,India

M.Tech scholer,Deptt.Of CSE,Doon Valley college of Engg. Karnal,India

Abstract

Histogram Equalization is one of the growing area of image enhancement in the field of digital image processing. Out of different histogram techniques Dualistic sub-image histogram equalization is one of the best method used in various fields. But after enhancement some noise may be present in output enhanced image i.e we do not get proper enhancing of image. Frame work of this paper is to show the presence of noise in enhanced image. This is done by comparing the different parameter like PSNR ,Contrast

,visualquality of image.

Keywords HE,PSNR,visual quqlity,contrast

1. Introduction

   Histogram is the probalistic distribution of gray levels in a digital image[1]Histogram Equalizat ion is used to obtain a uniform histogram for the output image image['2]wh ich give us a general overview of an image such as gray level distribution and its density, the average luminance of an image, image contrast, and so on.[8] HE is simp le and effective technique applied in many fields such as in medica l image processing, radar image processing, and sonar image processing. When an image is converted into digital form this process of digital processing can add some degradation in image which can be re moved by image enhancement[3]

   1.1

   Dualistic s ub-image histogram equalization method

   Some enhancement technique, change the luminance of the image significantly with the equalization, so it never be utilized in the video system. DSIHE technique for the enhancement is decomposed an image into two

   equal area sub-images on the bases of its gray level probability distribution function[4]. Then, these two images are taken in the equalizat ion process respectively. Then, after the enhancement these two sub-images are co mposed into one image. Finally, result of the enhancement provides a enhanced image with its original lu minance that make it possible to be used in video system directly.

   1. Wavelets thresholding

      De-noising can be accomplished using thresholding technique using a Daubechies wavelet order 4. The single trial was decomposed at level 5 and the detail coeffic ient were soft threshold [5],[6].

      All wavelet filters use wavelet thresholding operation for de-noising. The basic procedure for all thresholding method is as follows.

      Ca lculate the DWT o f the image.

      1. Threshold the wavelet coeffic ients

      2. Co mpute the IDWT to get the de-noised estimate.

      3. There are two thresholding function used, i.e. a hard threshold and a soft threshold.

The hard thresholding is described as

(w)= w I ( |W|>T) Where W is the wavelet coefficient

T is threshold

The soft thresholding function is described (w)= (w sgn (w) T) I( |W|>T

1. Implementation

   . Equal area Dualistic sub-image histogram equalization method [4]

   Algorithm Ste ps:

   Let us consider an input image X which is partitioned into two equal area sub- images X1 and X2 on the basis of median Xm. So we have X=X1 U X2. Here

   X1= {X(i,j) | X(i,j) < Xm, ¥ X(i,j) X }

   X2= {X(i,j) | X(i,j) Xm ¥ X(i.j) X }

   It is obvious that sub-image X1 is composed by gray level of {X0, X1, X2..Xm-1} and sub-image X2 is composed by gray level of

   {Xm, Xm+1.XL-1}

   Then the norma lized gray level PDF for both the sub images is

   {Pi / P, i = 0,1 ,2 ,3e-1 } and

   {Pi / (1-P) i = 0,1,2.L-1 }

   F2(Xk)= Xe+(Xl-1-Xe )c(Xk), k=e,e+1,..L-1

   For the final result of DSIHE, two sub-images are composed into one image. Suppose Y deno te the processed image, then

   Y = { Y(i,j) } = F1(X1) U F2 (X2)

   Finally this enhanced image is denoised with the wavelets thresholding technique.

2. Parameters 4.1Visual Quality

   With the taking a look at the enhance image, anyone can easily determine the difference between the input image and enhance image and hence, performance of the enhancement technique is evaluated

   1. Contrast

      It defines the difference between the lowest intensity level and highest intensity level. Higher the value of contrast means more diffe rence between the lowest and highest intensity level.

   2. Peak Signal To Noise Ratio (PSNR)

      It is the evaluation standard of the reconstructed image quality, is the most wanted feature [7]. PSNR is measured in the decibels (d B) and it is given by

      So the corresponding CDF is

      PSNR

      10log

      2552

      MSE

      C1(Xk) = ,k=0,1,. e -1

      C2(Xk) = , k=e ,e+1,.. L-1

      Based on the CDF function, the transfer function, for the two sub-images histogram equalization are

      F1(Xk)= X0+(Xe-1-X0)c (Xk), k=0,1,.e-1 .

      Where the value 255 is the ma ximu m possible value that can be attained by the image signal. Mean square error is defined as where M*N is the size of the orig inal image. Higher the PSNR value betters the reconstructed image.

3. Tool To Be Used

   In this imp le mentation of the different enhancement technique MATLAB 7.6 is used. From it image processing toolbox is used. Matlab is a high performance language for technical co mputing. It integrates computation, visualization and progra mming

   in easy to use environment where proble m and solution are e xpressed in fa miliar mathe matica l notation.

   4

   x 10

   histogram of original image

4. Experimental Results

   This result show the visual quality with the DSIHE enhancement technique

   18

   16

   14

   no. of pixel

   12

   10

   8

   6

   4

   2

   intensity

   0

   0 50 100 150 200 250

   FIGURE-H.1 ORIGINAL HISTOGRAM

   4

   x 10

   DSIHE histogram

   FIGURE-F.1 ORIGINAL IMAGE

   DSIHE image

   FIGURE-F.2 DSIHE IMAGE

   18

   16

   14

   no.of pixels

   12

   10

   8

   6

   4

   2

   intensity

   0

   0 50 100 150 200 250

   FIGURE-H.2 DSIHE HISTOGRAM

   4

   x 10

   14

   12

   10

   no. of pixels

   8

   6

   4

   2

   intensity

   0

   DSIHE denoised histogram

   [4]Y. Wang, Q. Chen, and B. Zhang, Soong-Der Chen, and Abd. Rahman Ramli, M inimum mean brightness error bi- histogram equalization in contrast enhancement, IEEE Transactions Consumer Electron. vol. 49, no. 4, pp. 1310- 1319, Nov. 2003
   [5]Rafael. E. Herrera, Robert J. Sclabassi, Single trial visual

   event related potential EEG analysis using wavelet transform proceedings of the first joint BM ES/EM B conference serving humanity advance technology Oct. 13-16, 99, ATLANTA USA.

   [6]S. Sudha, G.R.Suresh, and R. Sukanesh , Speckle Noise

   Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance, International Journal of Computer Theory and Engineering, Vol.1, No.1, April 2009.

   0 50 100 150 200 250

   FIGURE-H.3 DENOISE DSIHE

   Results

   If we co mpare the value of PSNR we have27.6239 dB for enhanced image and 54.7698 a fter denoising of DSIHE enhanced imge.

   If we co mpare the value of Contrast we have23.7566 for enhanced image and 0.0203 afte r denising of DSIHE enhanced imge.Th is shows the presence of noise in DSIHE technique of image enhancement.

5. Conclusion And Future Scope

   In this paper, a fra me work for denoising of Equal area dualistic sub-image histogram equalization (DSIHE)

   Technique of enhanced image is shown. Its give the

   satisfactory results for presence of noise.so better imple mentations are required to get enhancement of an image.

   In future, fo r taking the better result of the enhanced images diffe rent enhancement and denoising technique can be taken in various important fields where image enhancement is required like med ical image

   processing,news paper internetwoking ,sonar image processing and so on.

6. Refre nces

1. Fundamentals of digital image processing by A.K jain.

   University of California Davis

2. Rafael C. Gonzalez, and Richard E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, 2002

1. Wang Yuanji. Li Jianhua, Lu E, Fu Yao and Jiang Qinzhong, Image Quality Evaluation Based On Image Weighted Separating Block Peak Signal To Noise Ratio, IEEE Int. Conf. Neural Networks & Signal Processing,

   Nanjing, China, December 14-17, 2003

2. A. Rosenfeld, A. C. Kak. Digital Picture Processing, Academic Press, New York, 1976